logic

science

modelling
- an introduction

There are many reasons for drawing attention to modelling here. An
obvious one is that in asking people to consider an alternative logic,
it becomes easier if it is accepted that all logics are "just models" -
models of causality. And models do not have to be true as such, simply
useful in some pragmatic sense.

This is how we should view any scientific theory including biggies like
Darwinian
evolution, quantum mechanics or relativity. They are models of reality
and can be judged by the extent to which they
“work”.

Of course, what we mean by work then needs further examining. And here
I find Robert Rosen’s modelling relation or Howard
Pattee’s epistemic cut the most useful starting points. Or
going a bit further back, the semiotic approach of philosopher CS
Peirce on which pragmatism is based.

Anyway this is the epistemological
reason for emphasising modelling. If
all ideas about the world are “just models” then
even our ideas about why anything happens are just models of causality.
As long as we can form up our ideas with suitable crispness –
define them axiomatically – then we can consider the merits
of alternative logics.

But I have a second ontological
reason for devoting a section to
modelling. Epistemology is about how we can know things about the
world. Ontology is then what we what we come to believe to be actually
true – at least so far as the restrictions of modelling allow
us to know anything at all.

The ontological reason is that I believe a theory of modelling is also
going to be a theory of the mind. In its generalised way, it will be a
theory about cognition, knowing, control, semiosis, or any other grade
of "mindfulness" in a system.

So modelling theory is both a theory of human knowledge and also a more
general theory of how any system is organised to have top-down
“mindful” control. With pansemiosis, we can even
talk about how universes have minds, or at least memories, habits and
goals - the
laws of physics as they are more usually known.

Peirce, Rosen and Pattee provide a solid foundation for talking about
the modelling relation. But then of course
what I want to add here is a dichotomistic twist. I want to bring out
the fact that the modelling relation is dichotomistic (and hence a
“1,2,3” journey from vagueness to crisp
hierarchical order).

One of the key dichotomies is impressions~ideas. A mind develops by
forming a set of generalised or global ideas that then frame, or rather
constrain, the occurrence of localised impressions. A baby is born to a
blank buzzing confusion. It then learns how to interpret the world. It
develops the habits of perception that create a parade of crisp,
brightly felt and meaning-imbued, mental impressions.

Another basic dichotomy is truth~control. Once we start talking about
the purposes of modelling, examining what we mean when we say a model
“works”, we will see that it dichotomises into two
very different kinds of goals (which in turn are achieved by two very
different kinds of mental architecture).

One goal is to know the truth of the world. To be objective in other
words. To passively stand outside everything and see the whole of it.
The other goal is to be able to control everything. This is the
subjective pole of being. It is to be the “I” that
can – God-like – make things happen. We could also
say this is the contrast between the scientist as philosopher and the
scientist as technologist. Or between organic and mechanical logic
indeed.

The organic explains in holistic fashion. It is the more
complete view. But holism then pays the penalty of often seeming overly
complex - it delivers too much information, it requires too much
memory. The mechanical approach to explanation can be almost comic-book
reductionist. But it is
at least maximally efficient. It is guaranteed to deliver control with
the minimum of
information, the least anount of memory.

Anyway, we will see that our understanding of modelling can be guided
by a number of key
dichotomies such as impressions~ideas, control~truth,
technology~philosophy,
particulars~generals, mechanical~organic, subjective~objective,
epistemology~ontology, knower~known, self~other, matter~mind,
substance~form.

And note that here, as usual, local~global is our
uber-dichotomy so all these dichotomies are listed with the local term
first (like particular impressions or mechanistic control) and the
global term second (such as general ideas or organic knowing).